https://github.com/chaofengc/face-sketch-wild
Semi-Supervised Learning for Face Sketch Synthesis in the Wild, ACCV2018
https://github.com/chaofengc/face-sketch-wild
Last synced: 16 days ago
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Semi-Supervised Learning for Face Sketch Synthesis in the Wild, ACCV2018
- Host: GitHub
- URL: https://github.com/chaofengc/face-sketch-wild
- Owner: chaofengc
- License: mit
- Created: 2017-11-29T08:58:28.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2024-07-10T01:40:12.000Z (10 months ago)
- Last Synced: 2025-03-26T12:46:37.628Z (about 1 month ago)
- Language: Python
- Homepage:
- Size: 1.42 MB
- Stars: 88
- Watchers: 5
- Forks: 24
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Face Sketch Synthesis in the Wild
PyTorch implementation for face sketch synthesis in the wild through semi-supervised learning. Here is an example:
[**Semi-Supervised Learning for Face Sketch Synthesis in the Wild.**](https://arxiv.org/abs/1812.04929)
[Chaofeng Chen](https://chaofengc.github.io), [Wei Liu](http://www.visionlab.cs.hku.hk/people.html), [Xiao Tan](http://www.xtan.org/), [Kwan-Yee K. Wong](http://i.cs.hku.hk/~kykwong/).
# Getting Started
## Prerequisite
- Pytorch 0.3
- torchvision 0.2
- opencv-python
- matlab_wrapper
- Matlab (For FSIM evaluation)
## Datasets
- We use [CUFS](http://mmlab.ie.cuhk.edu.hk/archive/facesketch.html) and [CUFSF](http://mmlab.ie.cuhk.edu.hk/archive/cufsf/) dataset provided by Chinese University of Hong Kong (CUHK) to train our networks. You can also download them from [HERE](http://www.ihitworld.com/RSLCR.html).
- For the training of in the wild images, we use a subset of VGG-Face.
## Usage
### Download.
Download the datasets and pretrained models using the following scripts.
```
bash download_data_models.sh
```
Download the precalculated features for fast patch matching.
```
bash download_feature.sh
```
If the server is not available, you can also download the resources from [BaiduYun](https://pan.baidu.com/s/1pKpSVj7trJhxXVp7MoECaA) or [GoogleDrive](https://drive.google.com/drive/folders/1CxURCNxV1MbfYRNLq3PFcQkBouP0MSUX?usp=sharing), and then extract the files to the corresponding directory according to the download scripts above.
### Quick Test
After download the datasets and pretrain models, use the provided script to test the model
```
python test.py 1 # Test on CUFS test set
python test.py 2 # Test on CUFSF test set
python test.py 3 # Test on CUHK_Student test set
python test.py 4 # Test on VGG test set
```
You can also test on your own test dataset. Simply change the `--test_dir` and `--test_weight_path`. If you have ground truth images, you can also specify `--test_gt_dir`.
### Train
1. Configure training process.
- `vgg_select_num [0 or 10]`. `0`: no extra images in training. `10`: extra VGG-Face in training. **Only the largest vgg10 is provided here**.
- `train_style [cufs, cufsf]`. use `cufs` or `cufsf` as the reference style.
**The models in the paper are trained under 3 configurations**:
- `--vgg_select_num 0 --train_style cufs`. Model evaluated on CUFS.
- `--vgg_select_num 0 --train_style cufsf`. Model evaluated on CUFSF.
- `--vgg_select_num 10 --train_style cufs`. Model evaluated on VGG-Face.
2. Train the model.
```
python train.py
```
# Citation
If you find this code or the provided data useful in your research, please consider cite:
```
@inproceedings{chen2018face-sketch-wild,
title={Semi-Supervised Learning for Face Sketch Synthesis in the Wild},
author={Chen, Chaofeng and Liu, Wei and Tan, Xiao and Wong, Kwan-Yee~K.},
booktitle={Asian Conference on Computer Vision (ACCV)},
year={2018},
}
```
# Resources
[1] [Random Sampling and Locality Constraint for Face Sketch Synthesis](http://www.ihitworld.com/RSLCR.html)
[2] [Real-Time Exemplar-Based Face Sketch Synthesis](https://ybsong00.github.io/eccv14/index.html)